Security and Privacy Preservation in Vehicular Social Networks

Improving road safety and traffic efficiency has been a long-term endeavor for the government, automobile industry and academia. Recently, the U.S. Federal Communication Commission (FCC) has allocated a 75 MHz spectrum at 5.9 GHz for vehicular communications, opening a new door to combat the road fatalities by letting vehicles communicate to each other on the roads. Those communicating vehicles form a huge Ad Hoc Network, namely Vehicular Ad Hoc Network (VANET). In VANETs, a variety of applications ranging from the safety related (e.g. emergence report, collision warning) to the non-safety related (e.g., delay tolerant network, infortainment sharing) are enabled by vehicle-to-vehicle (V-2-V) and vehicle-to-roadside (V-2-I) communications. However, the flourish of VANETs still hinges on fully understanding and managing the challenging issues over which the public show concern, particularly, security and privacy preservation issues. If the traffic related messages are not authenticated and integrity-protected in VANETs, a single bogus and/or malicious message can potentially incur a terrible traffic accident. In addition, considering VANET is usually implemented in civilian scenarios where locations of vehicles are closely related to drivers, VANET cannot be widely accepted by the public if VANET discloses the privacy information of the drivers, i.e., identity privacy and location privacy. Therefore, security and privacy preservation must be well addressed prior to its wide acceptance. Over the past years, much research has been done on considering VANET's unique characteristics and addressed some security and privacy issues in VANETs; however, little of it has taken the social characteristics of VANET into consideration. In VANETs, vehicles are usually driven in a city environment, and thus we can envision that the mobility of vehicles directly reflects drivers' social preferences and daily tasks, for example, the places where they usually go for shopping or work. Due to these human factors in VANETs, not only the safety related applications but also the non-safety related applications will have some social characteristics. In this thesis, we emphasize VANET's social characteristics and introduce the concept of vehicular social network (VSN), where both the safety and non-safety related applications in VANETs are influenced by human factors including human mobility, human self-interest status, and human preferences. In particular, we carry on research on vehicular delay tolerant networks and infotainment sharing --- two important non-safety related applications of VSN, and address the challenging security and privacy issues related to them. The main contributions are, i) taking the human mobility into consideration, we first propose a novel social based privacy-preserving packet forwarding protocol, called SPRING, for vehicular delay tolerant network, which is characterized by deploying roadside units (RSUs) at high social intersections to assist in packet forwarding. With the help of high-social RSUs, the probability of packet drop is dramatically reduced and as a result high reliability of packet forwarding in vehicular delay tolerant network can be achieved. In addition, the SPRING protocol also achieves conditional privacy preservation and resist most attacks facing vehicular delay tolerant network, such as packet analysis attack, packet tracing attack, and black (grey) hole attacks. Furthermore, based on the ``Sacrificing the Plum Tree for the Peach Tree" --- one of the Thirty-Six Strategies of Ancient China, we also propose a socialspot-based packet forwarding (SPF) protocol for protecting receiver-location privacy, and present an effective pseudonyms changing at social spots strategy, called PCS, to facilitate vehicles to achieve high-level location privacy in vehicular social network; ii) to protect the human factor --- interest preference privacy in vehicular social networks, we propose an efficient privacy-preserving protocol, called FLIP, for vehicles to find like-mined ones on the road, which allows two vehicles sharing the common interest to identify each other and establish a shared session key, and at the same time, protects their interest privacy (IP) from other vehicles who do not share the same interest on the road. To generalize the FLIP protocol, we also propose a lightweight privacy-preserving scalar product computation (PPSPC) protocol, which, compared with the previously reported PPSPC protocols, is more efficient in terms of computation and communication overheads; and iii) to deal with the human factor -- self-interest issue in vehicular delay tolerant network, we propose a practical incentive protocol, called Pi, to stimulate self-interest vehicles to cooperate in forwarding bundle packets. Through the adoption of the proper incentive policies, the proposed Pi protocol can not only improve the whole vehicle delay tolerant network's performance in terms of high delivery ratio and low average delay, but also achieve the fairness among vehicles. The research results of the thesis should be useful to the implementation of secure and privacy-preserving vehicular social networks

Innovative Method of Combing Multidecade Remote Sensing Data for Detecting Precollapse Elevation Changes of Glaciers in the Larsen B Region, Antarctica

The Antarctic Peninsula has undergone dramatic changes in recent decades, including ice-shelf melting, disintegration, and retreat of the grounding line. The Larsen B ice shelf is of particular concern due to the unprecedented ice-shelf collapse in 2002. Since few observations on the Antarctic Peninsula were available before the 1970s, long-term investigation of the surface elevation change in the Larsen B region could not be pursued. In 1995, the United States administration declassified a collection of archived intelligence satellite photographs from the 1960s to the 1970s, including analogue satellite images from the ARGON program covering parts of the Larsen B region. We chose overlapping ARGON photos captured in the Larsen B region in 1963. These photos were all subjected to a tailored photogrammetric stereo-matching process, which overcomes those specific challenges related to the use of historical satellite images, such as poor image quality, low resolution, and a lack of high-precision validation data. We discovered that between 1963 and 2001, the surface elevations of the main tributary glaciers in the Larsen B embayment have undergone little change before the ice shelf collapse from 1963 to 2001 by comparing the reconstructed ARGON-derived digital elevation model (DEM) (1963) and ASTER-derived DEM (2001). In addition, the results demonstrated that the hierarchical image matching method can be modified and applied to reconstruct a historical Antarctic DEM using satellite images acquired & SIM;60 years ago through an innovative and rigorous ground control point selection procedure that guarantees no changes occurred at these points over the period. The new ARGON-derived DEM derived from ARGON (1963) can be used to build a long-term spatiotemporal record of observations for extended analyses of ice-surface dynamics and mass balance in the Larsen B region

Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and {promote trustworthy data dissemination} in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions{. To defend against free-riding attacks,} we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, {and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game} to identify malicious AVs {in disseminating fake information}. {Furthermore, for} lack of sufficient knowledge on parameters of {the} network model and user cost model {in dynamic game scenarios}, a two-tier reinforcement learning based algorithm with hotbooting is developed to obtain the optimal {strategies of subscriber AVs and publisher AVs with free-rider prevention}. Extensive simulations are conducted, and the results validate that our SPAD can effectively {prevent free-riders and enhance the dependability of disseminated contents,} compared with conventional schemes

A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks

The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in language comprehension and generation. However, the extremely high data and computational resource requirements for the performance of LLMs compel developers to resort to outsourcing training or utilizing third-party data and computing resources. These strategies may expose the model within the network to maliciously manipulated training data and processing, providing an opportunity for attackers to embed a hidden backdoor into the model, termed a backdoor attack. Backdoor attack in LLMs refers to embedding a hidden backdoor in LLMs that causes the model to perform normally on benign samples but exhibit degraded performance on poisoned ones. This issue is particularly concerning within communication networks where reliability and security are paramount. Despite the extensive research on backdoor attacks, there remains a lack of in-depth exploration specifically within the context of LLMs employed in communication networks, and a systematic review of such attacks is currently absent. In this survey, we systematically propose a taxonomy of backdoor attacks in LLMs as used in communication networks, dividing them into four major categories: input-triggered, prompt-triggered, instruction-triggered, and demonstration-triggered attacks. Furthermore, we conduct a comprehensive analysis of the benchmark datasets. Finally, we identify potential problems and open challenges, offering valuable insights into future research directions for enhancing the security and integrity of LLMs in communication networks

Efficient Strong Privacy-Preserving Conjunctive Keyword Search Over Encrypted Cloud Data

Searchable symmetric encryption (SSE) supports keyword search over outsourced symmetrically encrypted data. Dynamic searchable symmetric encryption (DSSE), a variant of SSE, further enables data updating. Most DSSE works with conjunctive keyword search primarily consider forward and backward privacy. Ideally, the server should only learn the result sets involving all keywords in the conjunction. However, existing schemes suffer from keyword pair result pattern (KPRP) leakage, revealing the partial result sets containing two of query keywords. We propose the first DSSE scheme to address aforementioned concerns that achieves strong privacy-preserving conjunctive keyword search. Specifically, our scheme can maintain forward and backward privacy and eliminate KPRP leakage, offering a higher level of security. The search complexity scales with the number of documents stored in the database in several existing schemes. However, the complexity of our scheme scales with the update frequency of the least frequent keyword in the conjunction, which is much smaller than the size of the entire database. Besides, we devise a least frequent keyword acquisition protocol to reduce frequent interactions between clients. Finally, we analyze the security of our scheme and evaluate its performance theoretically and experimentally. The results show that our scheme has strong privacy preservation and efficiency

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mild-temperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods.Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers